Accelerated acquisition of carotid MR angiography using 3D gradient-echo imaging with two-point Dixon.

This pilot study tests the feasibility of rapid carotid MR angiography using the liver acquisition with volume acceleration-flex technique (LAVA MRA). Seven healthy volunteers and 21 consecutive patients suspected of carotid stenosis underwent LAVA and conventional time-of-flight (cTOF) MRAs. Artery-to-fat and artery-to-muscle signal intensity ratios were manually measured. LAVA MRA exhibited a significantly larger artery-to-fat signal intensity ratio compared with cTOF MRA in all slices (P < 0.001) and exhibited a larger (P < 0.001) or equivalent (P = 1.0) artery-to-muscle signal intensity ratio in the extracranial carotid arteries. The image quality of the cervical carotid bifurcation and the signal change on each MRA were visually assessed and compared among the MRAs. There was no significant difference between the two MRAs in visual assessment. LAVA MRA can provide visualization similar to cTOF MRA in the evaluation of the cervical carotid bifurcation while reducing scan time by one-fifth.

Evaluation of white matter microstructure in patients with Parkinson's disease using microscopic fractional anisotropy.

PURPOSE: Micro fractional anisotropy (µFA) is more accurate than conventional fractional anisotropy (FA) for assessing microscopic tissue properties and can overcome limitations related to crossing white matter fibres. We compared µFA and FA for evaluating white matter changes in patients with Parkinson's disease (PD). METHODS: We compared FA and µFA measures between 25 patients with PD and 25 age- and gender-matched healthy controls using tract-based spatial statistics (TBSS) analysis. We also examined potential correlations between changes, revealed by conventional FA or µFA, and disease duration or Unified Parkinson's Disease Rating Scale (UPDRS)-III scores. RESULTS: Compared with healthy controls, patients with PD had significantly reduced µFA values, mainly in the anterior corona radiata (ACR). In the PD group, µFA values (primarily those from the ACR) were significantly negatively correlated with UPDRS-III motor scores. No significant changes or correlations with disease duration or UPDRS-III scores with tissue properties were detected using conventional FA. CONCLUSION: µFA can evaluate microstructural changes that occur during white matter degeneration in patients with PD and may overcome a key limitation of FA.

Ventricular volumetry and free-water corrected diffusion tensor imaging of the anterior thalamic radiation in idiopathic normal pressure hydrocephalus.

BACKGROUND AND PURPOSE: The pathophysiology of idiopathic normal pressure hydrocephalus (iNPH) has not been completely clarified. We investigated the brain structure in iNPH using automatic ventricular volumetry, single-tensor diffusion tensor imaging (DTI) and bi-tensor free-water (FW) imaging analyses while focusing on cognitive impairments before and after lumboperitoneal shunt surgery. MATERIALS AND METHODS: This retrospective study included 12 iNPH patients with structural magnetic resonance imaging (MRI) and diffusion MRI (dMRI) on a 3T-MRI scanner who underwent neuropsychological assessments before and after shunting and 8 healthy controls. Ventricular volumetry was conducted on structural MRI datasets using FreeSurfer. Ventricular volume was compared pre- and postoperatively. Correlation analyses were performed between ventricular volume or volume change and neuropsychological scores or score change. Tract-based spatial statistics were performed using dMRI datasets for group analyses between iNPH and controls and between pre- and post-surgery iNPH patients and for correlation analyses using neuropsychological scores. Tract-specific analyses were performed in the anterior thalamic radiation (ATR), followed by comparison and correlation analyses. RESULTS: The third ventricular volume was significantly decreased after shunting; its volume reduction negatively correlated with a neuropsychological improvement. Compared with controls, iNPH patients had lower fractional anisotropy and higher axial, radial, and mean diffusivities, and FW in the periventricular white matter including ATR, resulting in no difference in FW-corrected indices. Single-tensor DTI indices partially correlated with neuropsychological improvements, while FW-corrected indices had no correlations. CONCLUSION: Third ventricle enlargement is possibly linked to cognitive impairment and FW imaging possibly provides better white matter characterization in iNPH.

3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: Scan-rescan repeatability and comparison with conventional T1 -weighted images.

BACKGROUND: Previous quantitative synthetic MRI of the brain has been solely performed in 2D. PURPOSE: To evaluate the feasibility of the recently developed sequence 3D-QALAS for brain cortical thickness and volumetric analysis. STUDY TYPE: Reproducibility/repeatability study. SUBJECTS: Twenty-one healthy volunteers (35.6 ± 13.8 years). FIELD STRENGTH/SEQUENCE: 3D T1 -weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D-QALAS sequence was performed twice with a 3T scanner. ASSESSMENT: FreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan-rescan repeatability were evaluated for 3D-QALAS. STATISTICAL TESTS: Percent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan-rescan repeatability of the 3D-QALAS sequence-derived measurements. RESULTS: Percent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan-rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. DATA CONCLUSION: 3D-QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1834-1842.

White matter alterations in adult with autism spectrum disorder evaluated using diffusion kurtosis imaging.

PURPOSE: Autism spectrum disorder (ASD) is related to impairment in various white matter (WM) pathways. Utility of the recently developed two-compartment model of diffusion kurtosis imaging (DKI) to analyse axial diffusivity of WM is restricted by several limitations. The present study aims to validate the utility of model-free DKI in the evaluation of WM alterations in ASD and analyse the potential relationship between DKI-evident WM alterations and personality scales. METHODS: Overall, 15 participants with ASD and 15 neurotypical (NT) controls were scanned on a 3 T magnetic resonance (MR) scanner, and scores for autism quotient (AQ), systemising quotient (SQ) and empathising quotient (EQ) were obtained for both groups. Multishell diffusion-weighted MR data were acquired using two b-values (1000 and 2000 s/mm2). Differences in mean kurtosis (MK), radial kurtosis (RK) and axial kurtosis (AK) between the groups were evaluated using tract-based spatial statistics (TBSS). Finally, the relationships between the kurtosis indices and personality quotients were examined. RESULTS: The ASD group demonstrated significantly lower AK in the body and splenium of corpus callosum than the NT group; however, no other significant differences were identified. Negative correlations were found between AK and AQ or SQ, predominantly in WM areas related to social-emotional processing such as uncinate fasciculus, inferior fronto-occipital fasciculus, and inferior and superior longitudinal fasciculi. CONCLUSIONS: Model-free DKI and its indices may represent a novel, objective method for detecting the disease severity and WM alterations in patients with ASD.

Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.

PURPOSE: This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson's disease (PD) diagnosis. METHODS: NM-MRI datasets from two different 3T-scanners were used: a "principal dataset" with 122 participants and an "external validation dataset" with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method. We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined. RESULTS: For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient: U-net, 0.83 ± 0.04; inter-reader, 0.83 ± 0.04), but lower in external validation dataset (Dice coefficient: U-net, 079 ± 0.04; inter-reader, 0.85 ± 0.03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve: U-net, 0.950; manual, 0.948) and external (U-net, 0.944; manual, 0.931) datasets. CONCLUSION: U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method.

Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder.

Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that results from disruption of multiple neural circuits involved in emotional regulation. Although previous studies using diffusion tensor imaging (DTI) found smaller values of fractional anisotropy (FA) in the white matter, predominantly in the frontal lobe, of patients with MDD, studies using diffusion kurtosis imaging (DKI) are scarce. Here, we used DKI whole-brain analysis with tract-based spatial statistics (TBSS) to investigate the brain microstructural abnormalities in MDD. Twenty-six patients with MDD and 42 age- and sex-matched control subjects were enrolled. To investigate the microstructural pathology underlying the observations in DKI, a compartment model analysis was conducted focusing on the corpus callosum. In TBSS, the patients with MDD showed significantly smaller values of FA in the genu and frontal portion of the body of the corpus callosum. The patients also had smaller values of mean kurtosis (MK) and radial kurtosis (RK), but MK and RK abnormalities were distributed more widely compared with FA, predominantly in the frontal lobe but also in the parietal, occipital, and temporal lobes. Within the callosum, the regions with smaller MK and RK were located more posteriorly than the region with smaller FA. Model analysis suggested significantly smaller values of intra-neurite signal fraction in the body of the callosum and greater fiber dispersion in the genu, which were compatible with the existing literature of white matter pathology in MDD. Our results show that DKI is capable of demonstrating microstructural alterations in the brains of patients with MDD that cannot be fully depicted by conventional DTI. Though the issues of model validation and parameter estimation still remain, it is suggested that diffusion MRI combined with a biophysical model is a promising approach for investigation of the pathophysiology of MDD.

Radiologist involvement is associated with reduced use of MRI in the acute period of low back pain in a non-elderly population.

PURPOSE: To test the hypothesis that "acute-period" lumbar MRI in non-elderly patients with low back pain is less frequently performed at clinics/hospitals with greater involvement of full-time radiologists in the imaging workflow. METHODS: In a national-level claims database, we identified 14,819 non-elderly patients (mean age: 38.7±8.0 years) who visited clinics/hospitals for low back pain in 2013-2015. We classified the clinics/hospitals into four groups based on the level of full-time radiologist involvement and MRI ownership, and compared the frequency of acute-period lumbar MRI using hierarchical logistic regression analysis. RESULTS: Patients visiting facilities without a full-time radiologist (n=2105) were significantly (p<0.001) more likely to undergo acute-period MRI than those visiting facilities with ≥1 radiologist partially managing imaging workflow (level-1, n=491) or ≥1 radiologist intensively involved in imaging workflow (level-2, n=1190) (15.7% vs. 6.9% and 7.3%; adjusted odds ratio of no-radiologist versus level-2: 2.93, p=0.018). No difference was observed between level-1 and level-2 involvement. CONCLUSIONS: Facilities with no full-time radiologist were more likely to perform acute-period MRI to assess for low back pain, while no difference was seen between facilities with varying levels of radiologist involvement in the imaging workflow. Radiologist involvement may contribute to optimal utilisation of medical imaging. KEY POINTS: • Lumbar MRI was more frequently performed at facilities without full-time radiologists. • Full-time radiologists may play an important role in appropriate utilisation of imaging. • Frequency of MRI was similar between moderate and intensive radiologist involvement.

Noninvasive Computed Tomography-Derived Fractional Flow Reserve Based on Structural and Fluid Analysis: Reproducibility of On-site Determination by Unexperienced Observers.

OBJECTIVE: The aim of this study was to evaluate the reproducibility of computed tomography (CT)-derived fractional flow reserve (FFR) determined on site by inexperienced observers using a postprocessing software based on structural and fluid analysis. METHODS: Using 21 coronary vessels in 7 patients who underwent 320-row coronary CT angiography and catheter-FFR, 2 independent inexperienced observers (A: a student radiation technologist; B: a nonmedical staff) determined the CT-FFR using a postprocessing software. After a 20-minute training session, both observers postprocessed all vessels and readjusted their settings after another training/feedback. These CT-FFRs were compared with values determined by an expert analyst. RESULTS: The mean processing times were 23 ± 4 minutes (automatic), 71 ± 5 minutes (observer A), and 57 ± 7 minutes (observer B) per patient. The initial correlations with expert data were r = 0.92 (observer A) and 0.73 (observer B) and increased to 0.83 for observer B after additional training. The final absolute difference with the expert data was 0.000 to 0.020. The correlation between catheter-FFR and expert CT-FFR was r = 0.76. CONCLUSIONS: The CT-derived FFR on-site postprocessing software showed good reproducibility for measurements by inexperienced observers.

Prospective estimation of mean axon diameter and extra-axonal space of the posterior limb of the internal capsule in patients with idiopathic normal pressure hydrocephalus before and after a lumboperitoneal shunt by using q-space diffusion MRI.

OBJECTIVES: To prospectively estimate the mean axon diameter (MAD) and extracellular space of the posterior limb of the internal capsule (PLIC) in patients with idiopathic normal pressure hydrocephalus (iNPH) before and after a lumboperitoneal (LP) shunting operation using q-space diffusion MRI analysis. METHODS: We studied 12 consecutive patients with iNPH and 12 controls at our institution. After conventional magnetic resonance imaging (MRI), q-space image (QSI) data were acquired with a 3-T MRI scanner. The MAD and extra-axonal space of the PLIC before and after LP shunting were calculated using two-component q-space imaging analyses; the before and after values were compared. RESULTS: After LP shunt surgery, the extracellular space of the PLIC was significantly higher than that of the same patients before the operation (one-way analysis of variance (ANOVA) with Scheffé's post-hoc test, P = 0.024). No significant differences were observed in the PLIC axon diameters among normal controls or in patients before and after surgery. CONCLUSION: Increases in the root mean square displacement in the extra-axonal space of the PLIC in patients with iNPH after an LP shunt procedure are associated with the microstructural changes of white matter and subsequent abatement of patient symptoms. KEY POINTS: • Q-space diffusion MRI provides information on microstructural changes in the corticospinal tract • Lumboperitoneal (LP) shunting operation is useful for idiopathic normal pressure hydrocephalus • Q-space measurement may be a biomarker for the effect of the LP shunt procedure.

Rapid MR Angiography Using 3D Gradient-echo Imaging and the Two-point Dixon Method to Evaluate Carotid Plaque.

Liver acquisition with volume acceleration-flex (LAVA-Flex) acquires out-of-phase and in-phase echo images and automatically generates water-only and fat-only images from one single acquisition. The scan time of carotid MR angiography (MRA) using LAVA-Flex (LAVA MRA) is about one-fifth that of conventional time-of-flight MRA (cTOF MRA). We aimed to investigate whether LAVA MRA could provide useful information for the diagnosis of carotid plaque by utilizing the ability to acquire multiple sequences simultaneously. Comparing LAVA MRA and cTOF MRA images for carotid plaque, low-intensity plaques were more clearly identified in the in-phase images, and high-intensity plaques were more clearly identified in the water-only or out-of-phase images. None of the plaques exhibited superior visualization with the cTOF sequence. We concluded that LAVA MRA can provide more useful information on plaque evaluation using multiple sequences than cTOF MRA.

Reduced neurite density index in the prefrontal cortex of adults with autism assessed using neurite orientation dispersion and density imaging.

Background: Core symptoms of autism-spectrum disorder (ASD) have been associated with prefrontal cortex abnormalities. However, the mechanisms behind the observation remain incomplete, partially due to the challenges of modeling complex gray matter (GM) structures. This study aimed to identify GM microstructural alterations in adults with ASD using neurite orientation dispersion and density imaging (NODDI) and voxel-wise GM-based spatial statistics (GBSS) to reduce the partial volume effects from the white matter and cerebrospinal fluid. Materials and methods: A total of 48 right-handed participants were included, of which 22 had ASD (17 men; mean age, 34.42 ± 8.27 years) and 26 were typically developing (TD) individuals (14 men; mean age, 32.57 ± 9.62 years). The metrics of NODDI (neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISOVF]) were compared between groups using GBSS. Diffusion tensor imaging (DTI) metrics and surface-based cortical thickness were also compared. The associations between magnetic resonance imaging-based measures and ASD-related scores, including ASD-spectrum quotient, empathizing quotient, and systemizing quotient were also assessed in the region of interest (ROI) analysis. Results: After controlling for age, sex, and intracranial volume, GBSS demonstrated significantly lower NDI in the ASD group than in the TD group in the left prefrontal cortex (caudal middle frontal, lateral orbitofrontal, pars orbitalis, pars triangularis, rostral middle frontal, and superior frontal region). In the ROI analysis of individuals with ASD, a significantly positive correlation was observed between the NDI in the left rostral middle frontal, superior frontal, and left frontal pole and empathizing quotient score. No significant between-group differences were observed in all DTI metrics, other NODDI (i.e., ODI and ISOVF) metrics, and cortical thickness. Conclusion: GBSS analysis was used to demonstrate the ability of NODDI metrics to detect GM microstructural alterations in adults with ASD, while no changes were detected using DTI and cortical thickness evaluation. Specifically, we observed a reduced neurite density index in the left prefrontal cortices associated with reduced empathic abilities.

Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping.

Diffusion-weighted magnetic resonance imaging (dMRI) of brain has helped elucidate the microstructural changes of psychiatric and neurodegenerative disorders. Inconsistency between MRI models has hampered clinical application of dMRI-based metrics. Using harmonized dMRI data of 300 scans from 69 traveling subjects (TS) scanning the same individuals at multiple conditions with 13 MRI models and 2 protocols, the widely-used metrics such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were evaluated before and after harmonization with a combined association test (ComBat) or TS-based general linear model (TS-GLM). Results showed that both ComBat and TS-GLM significantly reduced the effects of the MRI site, model, and protocol for diffusion metrics while maintaining the intersubject biological effects. The harmonization power of TS-GLM based on TS data model is more powerful than that of ComBat. In conclusion, our research demonstrated that although ComBat and TS-GLM harmonization approaches were effective at reducing the scanner effects of the site, model, and protocol for DTI and NODDI metrics in WM, they exhibited high retainability of biological effects. Therefore, we suggest that, after harmonizing DTI and NODDI metrics, a multisite study with large cohorts can accurately detect small pathological changes by retaining pathological effects.

Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning.

PURPOSE: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy. METHODS: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases. RESULTS: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03). CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.

Comparison of Brain Volume Measurements Made with 0.3- and 3-T MR Imaging.

The volumes of intracranial tissues of 40 healthy volunteers acquired from 0.3- and 3-T scanners were compared using intraclass correlation coefficients, correlation analyses, and Bland-Altman analyses. We found high intraclass correlation coefficients, high Pearson's correlation coefficients, and low percentage biases in all tissues and most of the brain regions, although small differences were observed in some areas. These findings may support the validity of brain volumetry with low-field magnetic resonance imaging.

Neurite orientation dispersion and density imaging reveals white matter microstructural alterations in adults with autism.

BACKGROUND: Evidences suggesting the association between behavioral anomalies in autism and white matter (WM) microstructural alterations are increasing. Diffusion tensor imaging (DTI) is widely used to infer tissue microstructure. However, due to its lack of specificity, the underlying pathology of reported differences in DTI measures in autism remains poorly understood. Herein, we applied neurite orientation dispersion and density imaging (NODDI) to quantify and define more specific causes of WM microstructural changes associated with autism in adults. METHODS: NODDI (neurite density index [NDI], orientation dispersion index, and isotropic volume fraction [ISOVF]) and DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity, and radial diffusivity [RD]) measures were compared between autism (N = 26; 19 males and 7 females; 32.93 ± 9.24 years old) and age- and sex-matched typically developing (TD; N = 25; 17 males and 8 females; 34.43 ± 9.02 years old) groups using tract-based spatial statistics and region-of-interest analyses. Linear discriminant analysis using leave-one-out cross-validation (LDA-LOOCV) was also performed to assess the discriminative power of diffusion measures in autism and TD. RESULTS: Significantly lower NDI and higher ISOVF, suggestive of decreased neurite density and increased extracellular free-water, respectively, were demonstrated in the autism group compared with the TD group, mainly in commissural and long-range association tracts, but with distinct predominant sides. Consistent with previous reports, the autism group showed lower FA and higher MD and RD when compared with TD group. Notably, LDA-LOOCV suggests that NDI and ISOVF have relatively higher accuracy (82%) and specificity (NDI, 84%; ISOVF, 88%) compared with that of FA, MD, and RD (accuracy, 67-73%; specificity, 68-80%). LIMITATIONS: The absence of histopathological confirmation limit the interpretation of our findings. CONCLUSIONS: Our results suggest that NODDI measures might be useful as imaging biomarkers to diagnose autism in adults and assess its behavioral characteristics. Furthermore, NODDI allows interpretation of previous findings on changes in WM diffusion tensor metrics in individuals with autism.

Age-Related Changes in Relaxation Times, Proton Density, Myelin, and Tissue Volumes in Adult Brain Analyzed by 2-Dimensional Quantitative Synthetic Magnetic Resonance Imaging.

OBJECTIVES: Quantitative synthetic magnetic resonance imaging (MRI) enables the determination of fundamental tissue properties, namely, T1 and T2 relaxation times and proton density (PD), in a single scan. Myelin estimation and brain segmentation based on these quantitative values can also be performed automatically. This study aimed to reveal the changes in tissue characteristics and volumes of the brain according to age and provide age-specific reference values obtained by quantitative synthetic MRI. MATERIALS AND METHODS: This was a prospective study of healthy subjects with no history of brain diseases scanned with a multidynamic multiecho sequence for simultaneous measurement of relaxometry of T1, T2, and PD. We performed myelin estimation and brain volumetry based on these values. We performed volume-of-interest analysis on both gray matter (GM) and white matter (WM) regions for T1, T2, PD, and myelin volume fraction maps. Tissue volumes were calculated in the whole brain, producing brain parenchymal volume, GM volume, WM volume, and myelin volume. These volumes were normalized by intracranial volume to a brain parenchymal fraction, GM fraction, WM fraction, and myelin fraction (MyF). We examined the changes in the mean regional quantitative values and segmented tissue volumes according to age. RESULTS: We analyzed data of 114 adults (53 men and 61 women; median age, 66.5 years; range, 21-86 years). T1, T2, and PD values showed quadratic changes according to age and stayed stable or decreased until around 60 years of age and increased thereafter. Myelin volume fraction showed a reversed trend. Brain parenchymal fraction and GM fraction decreased throughout all ages. The approximation curves showed that WM fraction and MyF gradually increased until around the 40s to 50s and decreased thereafter. A significant decline in MyF was first noted in the 60s age group (Tukey test, P < 0.001). CONCLUSIONS: Our study showed changes according to age in tissue characteristic values and brain volumes using quantitative synthetic MRI. The reference values for age demonstrated in this study may be useful to discriminate brain disorders from healthy brains.

Guidelines for Management of Idiopathic Normal Pressure Hydrocephalus (Third Edition): Endorsed by the Japanese Society of Normal Pressure Hydrocephalus.

Among the various disorders that manifest with gait disturbance, cognitive impairment, and urinary incontinence in the elderly population, idiopathic normal pressure hydrocephalus (iNPH) is becoming of great importance. The first edition of these guidelines for management of iNPH was published in 2004, and the second edition in 2012, to provide a series of timely, evidence-based recommendations related to iNPH. Since the last edition, clinical awareness of iNPH has risen dramatically, and clinical and basic research efforts on iNPH have increased significantly. This third edition of the guidelines was made to share these ideas with the international community and to promote international research on iNPH. The revision of the guidelines was undertaken by a multidisciplinary expert working group of the Japanese Society of Normal Pressure Hydrocephalus in conjunction with the Japanese Ministry of Health, Labour and Welfare research project. This revision proposes a new classification for NPH. The category of iNPH is clearly distinguished from NPH with congenital/developmental and acquired etiologies. Additionally, the essential role of disproportionately enlarged subarachnoid-space hydrocephalus (DESH) in the imaging diagnosis and decision for further management of iNPH is discussed in this edition. We created an algorithm for diagnosis and decision for shunt management. Diagnosis by biomarkers that distinguish prognosis has been also initiated. Therefore, diagnosis and treatment of iNPH have entered a new phase. We hope that this third edition of the guidelines will help patients, their families, and healthcare professionals involved in treating iNPH.

A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.

PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning have been conducted. The aim of this study was to differentiate iNPH and AD using a residual extraction approach in the deep learning method. METHODS: Twenty-three patients with iNPH, 23 patients with AD and 23 healthy controls were included in this study. All patients and volunteers underwent brain MRI with a 3T unit, and we used only whole-brain three-dimensional (3D) T1-weighted images. We designed a fully automated, end-to-end 3D deep learning classifier to differentiate iNPH, AD and control. We evaluated the performance of our model using a leave-one-out cross-validation test. We also evaluated the validity of the result by visualizing important areas in the process of differentiating AD and iNPH on the original input image using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. RESULTS: Twenty-one out of 23 iNPH cases, 19 out of 23 AD cases and 22 out of 23 controls were correctly diagnosed. The accuracy was 0.90. In the Grad-CAM heat map, brain parenchyma surrounding the lateral ventricle was highlighted in about half of the iNPH cases that were successfully diagnosed. The medial temporal lobe or inferior horn of the lateral ventricle was highlighted in many successfully diagnosed cases of AD. About half of the successful cases showed nonspecific heat maps. CONCLUSION: Residual extraction approach in a deep learning method achieved a high accuracy for the differential diagnosis of iNPH, AD, and healthy controls trained with a small number of cases.

Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans.

OBJECTIVES: Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of various contrast-weighted images as well as simultaneous quantification of T1 and T2 relaxation times and proton density. However, to date, it has been challenging to generate magnetic resonance angiography (MRA) images with synthetic MRI. The purpose of this study was to develop a deep learning algorithm to generate MRA images based on 3D synthetic MRI raw data. MATERIALS AND METHODS: Eleven healthy volunteers and 4 patients with intracranial aneurysms were included in this study. All participants underwent a time-of-flight (TOF) MRA sequence and a 3D-QALAS synthetic MRI sequence. The 3D-QALAS sequence acquires 5 raw images, which were used as the input for a deep learning network. The input was converted to its corresponding MRA images by a combination of a single-convolution and a U-net model with a 5-fold cross-validation, which were then compared with a simple linear combination model. Image quality was evaluated by calculating the peak signal-to-noise ratio (PSNR), structural similarity index measurements (SSIMs), and high frequency error norm (HFEN). These calculations were performed for deep learning MRA (DL-MRA) and linear combination MRA (linear-MR), relative to TOF-MRA, and compared with each other using a nonparametric Wilcoxon signed-rank test. Overall image quality and branch visualization, each scored on a 5-point Likert scale, were blindly and independently rated by 2 board-certified radiologists. RESULTS: Deep learning MRA was successfully obtained in all subjects. The mean PSNR, SSIM, and HFEN of the DL-MRA were significantly higher, higher, and lower, respectively, than those of the linear-MRA (PSNR, 35.3 ± 0.5 vs 34.0 ± 0.5, P < 0.001; SSIM, 0.93 ± 0.02 vs 0.82 ± 0.02, P < 0.001; HFEN, 0.61 ± 0.08 vs 0.86 ± 0.05, P < 0.001). The overall image quality of the DL-MRA was comparable to that of TOF-MRA (4.2 ± 0.7 vs 4.4 ± 0.7, P = 0.99), and both types of images were superior to that of linear-MRA (1.5 ± 0.6, for both P < 0.001). No significant differences were identified between DL-MRA and TOF-MRA in the branch visibility of intracranial arteries, except for ophthalmic artery (1.2 ± 0.5 vs 2.3 ± 1.2, P < 0.001). CONCLUSIONS: Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time.